The present embodiments relate to predictive modeling for disease. For example, survival from lung cancer is predicted.
Survival or survivability from lung cancer, such as non-small cell lung cancer (NSCLC), is relatively low as compared to some other cancers. One common treatment is surgery to resect tumors. Accordingly, various prognosis techniques are directed to patients to be treated with surgery. However, these techniques may not apply to lung cancer patients treated with radiation and/or chemotherapy. An accurate personalized prediction of survival may stratify cancer patients into different risk groups and help in formulating more personalized treatment strategies.
Patients with stage I-IIIB lung cancer may be treated with curative intent without surgery. Currently, prediction of survival outcome for NSCLC patients treated with chemotherapy and/or radiotherapy is mainly based on clinical factors using TNM staging. However, clinical TNM staging may be inaccurate for survival prediction of non-surgical patients, and alternatives are currently lacking.
To improve risk stratification for non-surgical patients, a number of variables associated with survival have been identified. At present, the generally accepted prognostic factors for survival of inoperable patients are performance status, weight loss, presence of comorbidity, use of chemotherapy in addition to radiotherapy, and tumor size. Retrospective studies suggest that a higher radiation dose leads to improved local control and better survival rates. For other factors, such as sex and age, the literature shows inconsistent results, making it impossible to draw definitive conclusions.
Statistical and data-mining based models for predicting survival may have a promising predictive accuracy given that all the variables needed by the model are known. In reality, predictor variables are usually incomplete due to the data collection process, lack of accurate assessment and knowledge of tumor and patient related factors, or cost limitations related to equipment.